def setup_class(cls):
mmap = MoveMap()
mmap.set_bb(True)
cls.pose = Pose()
make_pose_from_sequence(cls.pose, 'PYTEST', "fa_standard", True)
cls.small_mv = SmallMover(mmap, 1., 1)
cls.rep_mv = RepeatMover(cls.small_mv, 3)
def test_set_attributes(self):
kT = 100.
n = 2
new_mm = MoveMap()
new_mm.set_bb(True)
self.shear_mv.temperature = kT
assert self.shear_mv.temperature == kT
self.shear_mv.nmoves = n
assert self.shear_mv.nmoves == n
self.shear_mv.movemap = new_mm
output_mmap = self.shear_mv.movemap(self.pose)
assert output_mmap.get_bb(1) is True
def setup_class(cls):
mmap = MoveMap()
cls.pose = Pose()
make_pose_from_sequence(cls.pose, 'PYTEST', "fa_standard", True)
cls.shear_mv = ShearMover(mmap, 1., 1)
def _minimize_step(sf, pose):
mmap = MoveMap()
mmap.set_bb(True)
mmap.set_chi(False)
mmap.set_jump(True)
min_mover = MinMover(mmap, sf, "lbfgs_armijo_nonmonotone", 0.0001, True)
min_mover.max_iter(1000)
min_mover.apply(pose)
class TestMinMover():
pose = get_pose('PYTEST')
mmap = MoveMap()
mmap.set_bb(True)
sfxn = get_score_function()
min_mv = MinMover(movemap=mmap, sfxn=sfxn)
def test_attributes(self):
assert self.min_mv.score_function() == self.min_mv.sfxn
assert self.min_mv.movemap(self.pose).get_bb(1)
def variation(eaObj, varcfg):
# setting the constant length of fragments being replaced
eaObj.varFragLength = int(varcfg['fragLength'])
# uses our previously defined value to set the fragment length of the fragment set in pyrosetta using data
eaObj.varFragments = core.fragment.ConstantLengthFragSet(
eaObj.varFragLength)
# reads our fragment file from the path generated using our previously defined values for the protein's id and the length specified for each fragment (3 or 9)
eaObj.varFragments.read_fragment_file(
eaObj.proteinPath +
"{0}/aat000_0{1}_05.200_v1_3".format(eaObj.pdbid, eaObj.varFragLength))
eaObj.movemap = MoveMap() # Enables movement of the abstract Pose object.
# Allows all backbone torsion angles to vary
eaObj.movemap.set_bb(int(varcfg['bbAngle']) == 1)
# Allows all side-chain torsion angles (χ) to vary
eaObj.movemap.set_chi(int(varcfg['chiAngle']) == 1)
eaObj.varMover = protocols.simple_moves.ClassicFragmentMover(
eaObj.varFragments, eaObj.movemap
) # Using the fragments extracted, it will move a Pose according to the allowed movement from MoveMap()
def relax(pose):
sf = create_score_function("ref2015")
sf.set_weight(rosetta.core.scoring.atom_pair_constraint, 5)
sf.set_weight(rosetta.core.scoring.dihedral_constraint, 1)
sf.set_weight(rosetta.core.scoring.angle_constraint, 1)
mmap = MoveMap()
mmap.set_bb(True)
mmap.set_chi(True)
mmap.set_jump(True)
relax = rosetta.protocols.relax.FastRelax()
relax.set_scorefxn(sf)
relax.max_iter(200)
relax.dualspace(True)
relax.set_movemap(mmap)
switch = SwitchResidueTypeSetMover("fa_standard")
switch.apply(pose)
relax.apply(pose)
def __init__(self, net, pose, fix=None, glycinate=False, max_iter=100,
n_moves=1, scorefxn=None, kT=0.1, k=15, dropout=0.5, relax=True):
super(NetPackMover, self).__init__(net, pose, k=k, dropout=dropout)
self.relax = relax
self.n_moves = n_moves
self.max_iter = max_iter
self.kT = kT
self.scorefxn = scorefxn
self.fix = fix if fix is not None else []
self.step = 0
self.fastrelax = ...
if relax or glycinate:
fastrelax = FastRelax()
fastrelax.set_scorefxn(self.scorefxn)
mm = MoveMap()
mm.set_bb(False)
mm.set_chi(True)
#for fixed in self.fix:
# mm.set_chi(fixed, False)
fastrelax.set_movemap(mm)
self.fastrelax = fastrelax
if glycinate:
self.dropout = 1.0
mask = torch.tensor([
1 - int(self.fixed_position(idx))
for idx in range(len(pose.sequence()))
], dtype=torch.bool)
for idx, residue in enumerate(pose.residues):
residue_name = self.sample_residue(pose, idx, mask=mask, argmax=True)
if not self.fixed_position(idx):
mutate_residue(pose, idx + 1, residue_name, pack_radius=10.0, pack_scorefxn=scorefxn)
mask[idx] = 0
self.dropout = dropout
self.fastrelax.apply(pose)
self.monte_carlo = MonteCarlo(pose, scorefxn, kT)
self.glycinate = glycinate
def sample_folding(sequence,
long_frag_filename,
long_frag_length,
short_frag_filename,
short_frag_length,
kT=3.0,
long_inserts=1,
short_inserts=3,
cycles=40,
jobs=1,
job_output='fold_output'):
"""
Performs
exporting structures to a PyMOL instance
Output structures are named <job_output>_(job#).pdb
"""
# 1. create a pose from the desired sequence (fullatom)
# the method pose_from_sequence produces a complete IDEALIZED
# protein conformation of the input sequence, the ResidueTypeSet (second
# argument below) may be varied, and this method supports non-proteogenic
# chemistry (though it is still a Rosetta Residue). however this syntax
# is more involved and not robust to user errors, and not presented here
# small differences in bond lengths and bond angles WILL change the results,
#### if you desire an alternate starting conformation, alter steps
#### 1. and 2. as you please
pose = pose_from_sequence(sequence, 'fa_standard')
# 2. linearize the pose by setting backbone torsions to large values
# the method make_pose_from_sequence does not create the new pose's
# PDBInfo object, so its done here, without it an error occurs later
pose.pdb_info(rosetta.core.pose.PDBInfo(pose.total_residue()))
for i in range(1, pose.total_residue() + 1):
pose.set_omega(i, 180)
pose.set_phi(i, -150) # reasonably straight
pose.set_psi(i, 150)
#### if you want to see the decoy scores, the PDBInfo needs these lines
#pose.pdb_info().chain(i, 'A') # necessary to color by score
#pose.pdb_info().number(i, i) # for PDB numbering
####
# 3. create a (fullatom) reference copy of the pose
test_pose = Pose()
test_pose.assign(pose)
test_pose.pdb_info().name('linearized pose')
# 4. create centroid <--> fullatom conversion Movers
to_centroid = SwitchResidueTypeSetMover('centroid')
# centroid Residue objects, of amino acids, have all their sidechain atoms
# replaced by a single representative "atom" to speed up calculations
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
# 5. convert the poses to centroid
to_centroid.apply(pose)
to_centroid.apply(test_pose)
# 6. create the MoveMap, all backbone torsions free
movemap = MoveMap()
movemap.set_bb(True)
# minimizing the centroid chi angles (the sidechain centroid atoms) is
# almost always USELESS since this compression is performed for speed,
# not accuracy and clashes usually occur when converting to fullatom
# 7. setup the ClassicFragmentMovers
# for the long fragments file
# this "try--except" is used to catch improper fragment files
try:
fragset_long = core.fragment.ConstantLengthFragSet(
long_frag_length, long_frag_filename)
#### the ConstantLengthFragSet is overloaded, this same
#### ConstantLengthFragSet can be obtained with different syntax
# to obtain custom fragments, see Generating Fragment Files below
except:
raise IOError('Make sure long_frag_length matches the fragments in\n\
long_frag_file and that long_frag_file is valid')
long_frag_mover = protocols.simple_moves.ClassicFragmentMover(
fragset_long, movemap)
# and for the short fragments file
# this "try--except" is used to catch improper fragment files
try:
fragset_short = core.fragment.ConstantLengthFragSet(
short_frag_length, short_frag_filename)
except:
raise IOError('Make sure short_frag_length matches the fragments in\n\
short_frag_file and that short_frag_file is valid')
short_frag_mover = protocols.simple_moves.ClassicFragmentMover(
fragset_short, movemap)
# 8. setup RepeatMovers for the ClassicFragmentMovers
insert_long_frag = protocols.moves.RepeatMover(long_frag_mover,
long_inserts)
insert_short_frag = protocols.moves.RepeatMover(short_frag_mover,
short_inserts)
# 9. create a PyMOL_Observer for exporting structures to PyMOL (optional)
# the PyMOL_Observer object owns a PyMOLMover and monitors pose objects for
# structural changes, when changes are detected the new structure is
# sent to PyMOL
# fortunately, this allows investigation of full protocols since
# intermediate changes are displayed, it also eliminates the need to
# manually apply the PyMOLMover during a custom protocol
# unfortunately, this can make the output difficult to interpret (since you
# aren't explicitly telling it when to export) and can significantly slow
# down protocols since many structures are output (PyMOL can also slow
# down if too many structures are provided and a fast machine may
# generate structures too quickly for PyMOL to read, the
# "Buffer clean up" message
# uncomment the line below to use PyMOL_Observer
## AddPyMOLObserver(test_pose, True)
# 10. create ScoreFunctions
# for low-resolution, centroid, poses necessary for the TrialMover's
# MonteCarlo object (see below)
scorefxn_low = create_score_function('score3')
# for high-resolution, fullatom, poses necessary for scoring final output
# from the PyJobDistributor (see below)
scorefxn_high = get_fa_scorefxn(
) # create_score_function('standard', 'score12')
# 11. setup a RepeatMover on a TrialMover of a SequenceMover
# -setup a TrialMover
# a. create a SequenceMover of the fragment insertions
#### add any other moves you desire
folding_mover = protocols.moves.SequenceMover()
folding_mover.add_mover(insert_long_frag)
folding_mover.add_mover(insert_short_frag)
# b. create a MonteCarlo object to define success/failure
# must reset the MonteCarlo object for each trajectory!
mc = MonteCarlo(test_pose, scorefxn_low, kT)
# c. create the TrialMover
trial = TrialMover(folding_mover, mc)
#### for each trajectory, try cycles number of applications
# -create the RepeatMover
folding = protocols.moves.RepeatMover(trial, cycles)
# 12. create a (Py)JobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn_high)
# 13. store the score evaluations for output
# printing the scores as they are produced would be difficult to read,
# Rosetta produces a lot of verbose output when running
scores = [0] * (jobs + 1)
scores[0] = scorefxn_low(pose)
# 14. perform folding by
counter = 0 # for exporting to PyMOL
while not jd.job_complete:
# a. set necessary variables for the new trajectory
# -reload the starting pose
test_pose.assign(pose)
# -change the pose's PDBInfo.name, for the PyMOL_Observer
counter += 1
test_pose.pdb_info().name(job_output + '_' + str(counter))
# -reset the MonteCarlo object (sets lowest_score to that of test_pose)
mc.reset(test_pose)
#### if you create a custom protocol, you may have additional
#### variables to reset, such as kT
#### if you create a custom protocol, this section will most likely
#### change, many protocols exist as single Movers or can be
#### chained together in a sequence (see above) so you need
#### only apply the final Mover
# b. apply the refinement protocol
folding.apply(test_pose)
####
# c. export the lowest scoring decoy structure for this trajectory
# -recover the lowest scoring decoy structure
mc.recover_low(test_pose)
# -store the final score for this trajectory
scores[counter] = scorefxn_low(test_pose)
# -convert the decoy to fullatom
# the sidechain conformations will all be default,
# normally, the decoys would NOT be converted to fullatom before
# writing them to PDB (since a large number of trajectories would
# be considered and their fullatom score are unnecessary)
# here the fullatom mode is reproduced to make the output easier to
# understand and manipulate, PyRosetta can load in PDB files of
# centroid structures, however you must convert to fullatom for
# nearly any other application
to_fullatom.apply(test_pose)
# -guess what cysteines are involved in disulfide bridges
guess_disulfides(test_pose)
# -output the fullatom decoy structure into a PDB file
jd.output_decoy(test_pose)
# -export the final structure to PyMOL
test_pose.pdb_info().name(job_output + '_' + str(counter) + '_fa')
#### if you want to see the decoy scores, uncomment the line below
#scorefxn_high( test_pose )
# 15. output the score evaluations
print('===== Centroid Scores =====')
print('Original Score\t:\t', scores[0])
for i in range(1, len(scores)): # print out the job scores
# the "[:14].ljust(14)" is to force the text alignment
print( (job_output + '_' + str( i ))[:14].ljust(14) +\
'\t:\t', scores[i] )
return scores # for other protocols
#Load and clean up pdb file
name = pdb_file + '.pdb'
cleaning.cleanATOM(name)
clean_name = pdb_file + ".clean.pdb"
#rna_set = core.chemical.ChemicalManager.get_instance().residue_type_set("coarse_rna").get()
#rna_set = core.chemical.ChemicalManager.get_instance().residue_type_set("fa_standard")
initial_pose = pose_from_pdb(clean_name)
print initial_pose
print toolbox.get_secstruct(initial_pose)
#Set up ScoreFunction
sf = get_fa_scorefxn()
#Set up MoveMap.
mm = MoveMap()
#change these for more or less flexability
mm.set_bb(True)
mm.set_chi(True)
#Pack and minimize initial pose to remove clashes.
pre_pre_packing_score = sf(initial_pose)
print "1"
task = standard_packer_task(initial_pose)
task.restrict_to_repacking()
task.or_include_current(True)
print "2"
pack_rotamers_mover = protocols.simple_moves.RotamerTrialsMover(sf, task)
pack_rotamers_mover.apply(initial_pose)
print "3"
scorefxn_high = protocols.loops.get_fa_scorefxn(
) # create_score_function_ws_patch( 'standard', 'score12' )
pymol = PyMOLMover() # If Pymol server is running, centroid stage will display
loop_begin = 145
loop_end = 155
loop_cut = 150
my_loop = protocols.loops.Loop(loop_begin, loop_end, loop_cut)
my_loops = protocols.loops.Loops()
my_loops.add_loop(my_loop)
print(my_loop)
protocols.loops.set_single_loop_fold_tree(p, my_loop)
movemap = MoveMap()
movemap.set_bb_true_range(loop_begin, loop_end)
movemap.set_chi(True)
print(p.fold_tree())
print("setting up movers")
# use the KinematicMover explicitly in centroid stage
kic_mover = KinematicMover()
#centroid/fullatom conversion movers
to_centroid = protocols.simple_moves.SwitchResidueTypeSetMover('centroid')
to_fullatom = protocols.simple_moves.SwitchResidueTypeSetMover('fa_standard')
recover_sidechains = protocols.simple_moves.ReturnSidechainMover(starting_p)
#set up sidechain packer movers
def sample_docking(pdb_filename, partners,
translation = 3.0, rotation = 8.0,
jobs = 1, job_output = 'dock_output'):
"""
Performs protein-protein docking using the Rosetta standard DockingProtocol
on the proteins in <pdb_filename> using the relative chain
<partners> with an initial perturbation using <translation>
Angstroms and <rotation> degrees. <jobs> trajectories are performed
with output structures named <job_output>_(job#).pdb.
structures are exported to a PyMOL instance.
"""
# 1. creates a pose from the desired PDB file
pose = Pose()
pose_from_file(pose, pdb_filename)
# 2. setup the docking FoldTree
# using this method, the jump number 1 is automatically set to be the
# inter-body jump
dock_jump = 1
# the exposed method setup_foldtree takes an input pose and sets its
# FoldTree to have jump 1 represent the relation between the two docking
# partners, the jump points are the residues closest to the centers of
# geometry for each partner with a cutpoint at the end of the chain,
# the second argument is a string specifying the relative chain partners
# such as "A_B" of "LH_A", ONLY TWO BODY DOCKING is supported and the
# partners MUST have different chain IDs and be in the same pose (the
# same PDB), additional chains can be grouped with one of the partners,
# the "_" character specifies which bodies are separated
# the third argument...is currently unsupported but must be set (it is
# supposed to specify which jumps are movable, to support multibody
# docking...but Rosetta doesn't currently)
# the FoldTrees setup by this method are for TWO BODY docking ONLY!
protocols.docking.setup_foldtree(pose, partners, Vector1([dock_jump]))
# 3. create centroid <--> fullatom conversion Movers
to_centroid = SwitchResidueTypeSetMover('centroid')
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
# and a Mover to recover sidechain conformations
# when a protocol samples backbone torsion space in centroid,
# the sidechain conformations are neglected, when it is transferred
# to fullatom, we typically set the sidechain conformations to their
# "original" values and perform sidechain packing,
# a ReturnSidechainMover saves a pose's sidechains (in this case
# staring_pose) and when applied, inserts these conformations
# into the input pose
recover_sidechains = protocols.simple_moves.ReturnSidechainMover(pose)
# 4. convert to centroid
to_centroid.apply(pose)
# 5. create a (centroid) test pose
test_pose = Pose()
test_pose.assign(pose)
# 6. create ScoreFunctions for centroid and fullatom docking
scorefxn_low = create_score_function('interchain_cen')
scorefxn_high = create_score_function('docking')
# PyRosetta3: scorefxn_high_min = create_score_function_ws_patch('docking', 'docking_min')
scorefxn_high_min = create_score_function('docking', 'docking_min')
# 7. create Movers for producing an initial perturbation of the structure
# the DockingProtocol (see below) can do this but several Movers are
# used to demonstrate their syntax
# these Movers randomize the orientation (rotation) of each docking partner
randomize_upstream = RigidBodyRandomizeMover(pose, dock_jump,
partner_upstream)
randomize_downstream = RigidBodyRandomizeMover(pose, dock_jump,
partner_downstream)
# this Mover translates one docking partner away from the other in a random
# direction a distance specified by the second argument (in Angstroms)
# and rotates this partner randomly by the third argument (in degrees)
dock_pert = RigidBodyPerturbMover(dock_jump, translation, rotation)
# this Mover randomizes a pose's partners (rotation)
spin = RigidBodySpinMover(dock_jump)
# this Mover uses the axis defined by the inter-body jump (jump 1) to move
# the docking partners close together
slide_into_contact = protocols.docking.DockingSlideIntoContact(dock_jump)
# 8. setup the MinMover
# the MoveMap can set jumps (by jump number) as degrees of freedom
movemap = MoveMap()
movemap.set_jump(dock_jump, True)
# the MinMover can minimize score based on a jump degree of freedom, this
# will find the distance between the docking partners which minimizes
# the score
minmover = protocols.minimization_packing.MinMover()
minmover.movemap(movemap)
minmover.score_function(scorefxn_high_min)
# 9. create a SequenceMover for the perturbation step
perturb = protocols.moves.SequenceMover()
perturb.add_mover(randomize_upstream)
perturb.add_mover(randomize_downstream)
perturb.add_mover(dock_pert)
perturb.add_mover(spin)
perturb.add_mover(slide_into_contact)
perturb.add_mover(to_fullatom)
perturb.add_mover(recover_sidechains)
perturb.add_mover(minmover)
# 10. setup the DockingProtocol
# ...as should be obvious by now, Rosetta applications have no central
# standardization, the DockingProtocol object can be created and
# applied to perform Rosetta docking, many of its options and settings
# can be set using the DockingProtocol setter methods
# here, on instance is created with all default values and the movable jump
# is manually set to jump 1 (just to be certain), the centroid docking
# ScoreFunction is set and the fullatom docking ScoreFunction is set
dock_prot = protocols.docking.DockingProtocol() # contains many docking functions
dock_prot.set_movable_jumps(Vector1([1])) # set the jump to jump 1
dock_prot.set_lowres_scorefxn(scorefxn_low)
dock_prot.set_highres_scorefxn(scorefxn_high_min)
#### you can alternatively access the low and high resolution sections of
#### the DockingProtocol, both are applied by the DockingProtocol but
#### a novel protocol may only require centroid (DockingLowRes) or
#### fullatom (DockingHighRes), uncomment the lines below and their
#### application below
#docking_low = DockingLowRes()
#docking_low.set_movable_jumps(Vector1([1]))
#docking_low.set_scorefxn(scorefxn_low)
#docking_high = DockingHighRes()
#docking_high.set_movable_jumps(Vector1([1]))
#docking_high.set_scorefxn(scorefxn_high)
# 11. setup the PyJobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn_high)
temp_pose = Pose() # a temporary pose to export to PyMOL
temp_pose.assign(pose)
to_fullatom.apply(temp_pose) # the original pose was fullatom
recover_sidechains.apply(temp_pose) # with these sidechains
jd.native_pose = temp_pose # for RMSD comparison
# 12. setup a PyMOL_Observer (optional)
# the PyMOL_Observer object owns a PyMOLMover and monitors pose objects for
# structural changes, when changes are detected the new structure is
# sent to PyMOL
# fortunately, this allows investigation of full protocols since
# intermediate changes are displayed, it also eliminates the need to
# manually apply the PyMOLMover during a custom protocol
# unfortunately, this can make the output difficult to interpret (since you
# aren't explicitly telling it when to export) and can significantly slow
# down protocols since many structures are output (PyMOL can also slow
# down if too many structures are provided and a fast machine may
# generate structures too quickly for PyMOL to read, the
# "Buffer clean up" message
# uncomment the line below to use the PyMOL_Observer
## AddPyMOLObserver(test_pose, True)
# 13. perform protein-protein docking
counter = 0 # for pretty output to PyMOL
while not jd.job_complete:
# a. set necessary variables for this trajectory
# -reset the test pose to original (centroid) structure
test_pose.assign(pose)
# -change the pose name, for pretty output to PyMOL
counter += 1
test_pose.pdb_info().name(job_output + '_' + str(counter))
# b. perturb the structure for this trajectory
perturb.apply(test_pose)
# c. perform docking
dock_prot.apply(test_pose)
#### alternate application of the DockingProtocol pieces
#docking_low.apply(test_pose)
#docking_high.apply(test_pose)
# d. output the decoy structure
to_fullatom.apply(test_pose) # ensure the output is fullatom
# to PyMOL
test_pose.pdb_info().name(job_output + '_' + str( counter ) + '_fa')
# to a PDB file
jd.output_decoy(test_pose)
def mutate(pdb_name, n_muts=100):
OUT = open(mydir + '/data/deltas.txt', 'w')
#takes name of pdb file without the extention
init(extra_options='-mute basic -mute core')
# Constants
PACK_RADIUS = 10.0
#Amino acids, notice there is no C
AAs = ("A", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q",
"R", "S", "T", "V", "W", "Y")
#Number of mutations to accept
max_accept_mut = 600
#Population size
N = 100
#Beta (temp term)
beta = 1
pdb = pdb_name + ".pdb"
cleanATOM(pdb)
pdb_clean_name = pdb_name + '.clean.pdb'
initial_pose = pose_from_pdb(pdb_clean_name)
#Set up ScoreFunction
sf = get_fa_scorefxn()
#Set up MoveMap.
mm = MoveMap()
#change these for more or less flexability
mm.set_bb(True)
mm.set_chi(True)
#Pack and minimize initial pose to remove clashes.
pre_pre_packing_score = sf(initial_pose)
task = standard_packer_task(initial_pose)
task.restrict_to_repacking()
task.or_include_current(True)
pack_rotamers_mover = RotamerTrialsMover(sf, task)
pack_rotamers_mover.apply(initial_pose)
min_mover = MinMover()
min_mover.movemap(mm)
min_mover.score_function(sf)
min_mover.min_type('dfpmin_armijo_nonmonotone')
min_mover.apply(initial_pose)
post_pre_packing_score = sf(initial_pose)
#Set threshold for selection
threshold = pre_pre_packing_score / 2
#number of residues to select from
n_res = initial_pose.total_residue()
max_accept_mut = 1500
#start sim
i = 0
gen = 0
columns = ['Number', 'Selection', 'P_fix', 'P_fix_MH']
print >> OUT, '\t'.join(columns)
while i < max_accept_mut:
#update the number of generations that have pased
gen += 1
#pick a place to mutate
mut_location = random.randint(1, n_res)
#get the amino acid at that position
res = initial_pose.residue(mut_location)
#don't mess with C, just choose again
if res.name1() == 'C':
mut_location = random.randint(1, n_res)
#get the amino acid at that position
res = initial_pose.residue(mut_location)
#choose the amino acid to mutate to
new_mut_key = random.randint(0, len(AAs) - 1)
proposed_res = AAs[new_mut_key]
#don't bother mutating to the same amino acid it just takes more time
if proposed_res == res.name1():
new_mut_key = random.randint(0, len(AAs) - 1)
proposed_res = AAs[new_mut_key]
#make the mutation
mutant_pose = mutate_residue(initial_pose, mut_location, proposed_res,
PACK_RADIUS, sf)
#score mutant
variant_score = sf(mutant_pose)
probability = calc_prob_fix(variant_score, post_pre_packing_score, N,
beta, threshold)
probability_mh = calc_prob_mh(variant_score, post_pre_packing_score, N,
beta, threshold)
f_i = calc_x_fix(post_pre_packing_score, beta, threshold)
f_j = calc_x_fix(variant_score, beta, threshold)
s = math.log(f_j) - math.log(f_i)
#test to see if mutation is accepted
if np.isnan(probability) == True:
continue
rndm = random.random()
if (i < 100):
if (rndm < probability):
initial_pose = mutant_pose
post_pre_packing_score = variant_score
else:
continue
# Assuming 100 burn in phase, take this if out if you want to store everything
else:
data = [str(i), str(s), str(probability), str(probability_mh)]
print >> OUT, '\t'.join(data)
#save name and energy change
#data.append(variant_name + "," + str(variant_score) + "," + str(variant_score - post_pre_packing_score) + "," + str(probability) + "," + str(gen) + "\n")
#pdb_name=str(i)+".pdb"
#mutant_pose.dump_pdb(pdb_name)
print i, s, probability, rndm
#update number of accepts
i += 1
OUT.close()
test = Pose()
test.assign(start)
start.pdb_info().name("start")
test.pdb_info().name("test")
pmm = PyMOLMover()
pmm.apply(start)
pmm.apply(test)
pmm.keep_history(True)
print( pmm )
# Small and Shear Moves
kT = 1.0
n_moves = 1
movemap = MoveMap()
movemap.set_bb(True)
small_mover = SmallMover(movemap, kT, n_moves)
shear_mover = ShearMover(movemap, kT, n_moves)
small_mover.angle_max("H", 5)
small_mover.angle_max("E", 5)
small_mover.angle_max("L", 5)
small_mover.apply(test)
print( small_mover )
print( shear_mover )
test2 = Pose()
test2.assign(start)
test2.pdb_info().name("test2")
init(
extra_options="-constant_seed"
) # WARNING: option '-constant_seed' is for testing only! MAKE SURE TO REMOVE IT IN PRODUCTION RUNS!!!!!
import os
os.chdir('.test.output')
print('Refinement ----------------------------------------------')
kT = 1.0
n_moves = 10
pose = core.import_pose.pose_from_file("../test/data/test_fragments.pdb")
pose_frag = core.import_pose.pose_from_file("../test/data/test_fragments.pdb")
print('setting up a move map')
movemap = MoveMap()
print('setting all backbone movement to true, and residue 10 to false')
movemap.set_bb(True)
movemap.set_bb(10, False)
print('outputting movemap')
movemap.show(pose.total_residue())
fragset3mer = core.fragment.ConstantLengthFragSet(
3, "../test/data/test3_fragments") # "aatestA03_05.200_v1_3")
fragset9mer = core.fragment.ConstantLengthFragSet(
9, "../test/data/test9_fragments") # "aatestA09_05.200_v1_3")
print('mover: ClassicFragmentMover, 3mer')
movemap.set_bb(True)
mover_3mer = protocols.simple_moves.ClassicFragmentMover(fragset3mer, movemap)
mover_3mer.apply(pose_frag)
def movemap(pose, PDB_out=False):
"""
Demonstrates the syntax necessary for basic usage of the MoveMap object
performs these changes with a demonstrative backbone minimization
using <pose> and writes structures to PDB files if <PDB_out> is True
"""
#########
# MoveMap
# a MoveMap encodes what data is allowed to change in a Pose, referred to as
# its degrees of freedom
# a MoveMap is separate from a Pose and is usually required by a Mover so
# that the correct degrees of freedom are manipulated, in this way,
# MoveMap and Pose objects often work in parallel
# several MoveMap's can correspond to the same Pose
# a MoveMap stores information on a per-residue basis about the
# backbone ({phi, psi, omega}) and chi ({chi_i}) torsion angle sets
# the MoveMap can only set these sets of torsions to True or False, it
# cannot set freedom for the individual angles (such as phi free and psi
# fixed)
# the MoveMap has no upper-limit on its residue information, it defaults to
# all residues (up to residue 99999999) backbone and chi False
# you can view the MoveMap per-residue torsion settings by using the
# MoveMap.show( Pose.total_residue() ) method (the input argument is the
# highest residue to output, it does not support viewing a range)
pose_move_map = MoveMap()
# change all backbone torsion angles
pose_move_map.set_bb(True)
# change all chi angle torsion angles (False by default)
pose_move_map.set_chi(False)
# change a single backbone torsion angles
#pose_move_map.set_bb(1, True) # example syntax
# change a single residue's chi torsion angles
#pose_move_map.set_chi(1, True) # example syntax
pose_move_map.show(pose.total_residue())
# perform gradient based minimization on the "median" residues, this
# method (MinMover) determines the gradient of an input pose using a
# ScoreFunction for evaluation and a MoveMap to define the degrees of
# freedom
# create a standard ScoreFunction
scorefxn = get_fa_scorefxn(
) # create_score_function_ws_patch('standard', 'score12')
# redefine the MoveMap to include the median half of the residues
# turn "off" all backbone torsion angles
pose_move_map.set_bb(False) # reset to backbone False
# turn "on" a range of residue backbone torsion angles
pose_move_map.set_bb_true_range(int(pose.total_residue() / 4),
int(pose.total_residue() * 3 / 4))
# create the MinMover
minmover = protocols.minimization_packing.MinMover()
minmover.score_function(scorefxn)
minmover.movemap(pose_move_map)
# create a copy of the pose
test_pose = Pose()
test_pose.assign(pose)
# apply minimization
scorefxn(test_pose) # to prevent verbose output on the next line
pymover = PyMOLMover()
#### uncomment the line below and "comment-out" the two lines below to
#### export the structures into different PyMOL states of the same object
#pymover.keep_history = True # enables viewing across states
#### comment-out the line below, changing PDBInfo names tells the
#### PyMOLMover to produce new objects
test_pose.pdb_info().name('original')
pymover.apply(test_pose)
print('\nPre minimization score:', scorefxn(test_pose))
minmover.apply(test_pose)
if PDB_out:
test_pose.dump_pdb('minimized.pdb')
print('Post minimization score:', scorefxn(test_pose))
#### comment-out the line below
test_pose.pdb_info().name('minimized')
pymover.apply(test_pose)
def sample_refinement(pdb_filename,
kT=1.0,
smallmoves=3,
shearmoves=5,
backbone_angle_max=7,
cycles=9,
jobs=1,
job_output='refine_output'):
"""
Performs fullatom structural refinement on the input <pdb_filename> by
perturbing backbone torsion angles with a maximum perturbation of
<backbone_angle_max> for <cycles> trials of
<smallmoves> perturbations of a random residue's phi or psi and
<shearmoves> perturbations of a random residue's phi and the preceding
residue's psi followed by gradient based backbone torsion angle
minimization and sidechain packing with an acceptance criteria scaled
by <kT>. <jobs> trajectories are performed, continually exporting
structures to a PyMOL instance.
Output structures are named <job_output>_(job#).pdb.
"""
# 1. create a pose from the desired PDB file
pose = Pose()
pose_from_file(pose, pdb_filename)
# 2. create a reference copy of the pose in fullatom
starting_pose = Pose()
starting_pose.assign(pose)
# 3. create a standard ScoreFunction
#### implement the desired ScoreFunction here
scorefxn = get_fa_scorefxn() # create_score_function('standard')
#### If you wish to use the ClassRelax protocol, uncomment the following
#### line and comment-out the protocol setup below
#refinement = protocols.relax.ClassicRelax( scorefxn )
#### Setup custom high-resolution refinement protocol
#### backbone refinement protocol
# 4. create a MoveMap, all backbone torsions free
movemap = MoveMap()
movemap.set_bb(True)
# 5. create a SmallMover
# a SmallMover perturbs a random (free in the MoveMap) residue's phi or psi
# torsion angle for an input number of times and accepts of rejects this
# change based on the Metropolis Criteria using the "rama" ScoreType and
# the parameter kT
# set the maximum angle to backbone_angle_max, apply it smallmoves times
smallmover = protocols.simple_moves.SmallMover(movemap, kT, smallmoves)
# angle_max is secondary structure dependent, however secondary structure
# has not been evaulated in this protocol, thus they are all set
# to the same value0
smallmover.angle_max(backbone_angle_max) # sets all at once
#### use the overloaded version of the SmallMover.angle_max method if you
#### want to use secondary structure biased moves
#smallmover.angle_max('H', backbone_angle_max)
#smallmover.angle_max('E', backbone_angle_max)
#smallmover.angle_max('L', backbone_angle_max)
# 6. create a ShearMover
# a ShearMover is identical to a SmallMover except that the angles perturbed
# are instead a random (free in the MoveMap) residue's phi and the
# preceding residue's psi, this reduces the downstream structural change
# set the maximum angle to backbone_angle_max, apply it shearmoves times
shearmover = protocols.simple_moves.ShearMover(movemap, kT, shearmoves)
# same angle_max restictions as SmallMover
shearmover.angle_max(backbone_angle_max)
#### use the overloaded version of the SmallMover.angle_max method if you
#### want to use secondary structure biased moves
#shearmover.angle_max('H', backbone_angle_max)
#shearmover.angle_max('E', backbone_angle_max)
#shearmover.angle_max('L', backbone_angle_max)
# 7. create a MinMover, for backbone torsion minimization
minmover = protocols.minimization_packing.MinMover()
minmover.movemap(movemap)
minmover.score_function(scorefxn)
#### sidechain refinement protocol, simple packing
# 8. setup a PackRotamersMover
to_pack = standard_packer_task(starting_pose)
to_pack.restrict_to_repacking() # prevents design, packing only
to_pack.or_include_current(True) # considers the original sidechains
packmover = protocols.minimization_packing.PackRotamersMover(
scorefxn, to_pack)
#### assess the new structure
# 9. create a PyMOLMover
pymover = PyMOLMover()
# uncomment the line below to load structures into successive states
#pymover.keep_history(True)
#### the PyMOLMover slows down the protocol SIGNIFICANTLY but provides
#### very informative displays
#### the keep_history flag (when True) tells the PyMOLMover to store new
#### structures into successive states, for a single trajectory, this
#### allows you to see intermediate changes (depending on where the
#### PyMOLMover is applied), when using a JobDistributor or otherwise
#### displaying multiple trajectories with a single protocol, the output
#### can get confusing to interpret, by changing the pose's PDBInfo.name
#### the structure will load into a new PyMOL state
#### try uncommenting the lines below to see different output
#pymover.update_energy(True) # see the total score in color
# 10. export the original structure, and scores, to PyMOL
pymover.apply(pose)
scorefxn(pose)
pymover.send_energy(pose)
# 11. setup a RepeatMover on a TrialMover of a SequenceMover (wow!)
# -setup a TrialMover
# a. create a SequenceMover of the previous moves
#### add any other moves you desire
combined_mover = SequenceMover()
combined_mover.add_mover(smallmover)
combined_mover.add_mover(shearmover)
combined_mover.add_mover(minmover)
combined_mover.add_mover(packmover)
#### explore the protocol using the PyMOLMover, try viewing structures
#### before they are accepted or rejected
combined_mover.add_mover(pymover)
# b. create a MonteCarlo object to define success/failure
mc = MonteCarlo(pose, scorefxn, kT) # must reset for each trajectory!
# c. create the TrialMover
trial = TrialMover(combined_mover, mc)
#### explore the protocol using the PyMOLMover, try viewing structures
#### after acceptance/rejection, comment-out the lines below
#original_trial = TrialMover(combined_mover, mc)
#trial = SequenceMover()
#trial.add_mover(original_trial)
#trial.add_mover(pymover)
#### for each trajectory, try cycles number of applications
# -create the RepeatMover
refinement = RepeatMover(trial, cycles)
####
# 12. create a (Py)JobDistributor
jd = PyJobDistributor(job_output, jobs, scorefxn)
jd.native_pose = starting_pose
# 13. store the score evaluations for output
# printing the scores as they are produced would be difficult to read,
# Rosetta produces a lot of verbose output when running
scores = [0] * (jobs + 1)
scores[0] = scorefxn(starting_pose)
# 14. perform the refinement protocol
counter = 0 # for exporting to PyMOL
while not jd.job_complete:
# a. set necessary variables for the new trajectory
# -reload the starting pose
pose.assign(starting_pose)
# -change the pose's PDBInfo.name, for the PyMOLMover
counter += 1
pose.pdb_info().name(job_output + '_' + str(counter))
# -reset the MonteCarlo object (sets lowest_score to that of p)
mc.reset(pose)
#### if you create a custom protocol, you may have additional
#### variables to reset, such as kT
#### if you create a custom protocol, this section will most likely
#### change, many protocols exist as single Movers or can be
#### chained together in a sequence (see above) so you need
#### only apply the final Mover
# b. apply the refinement protocol
refinement.apply(pose)
####
# c. output the lowest scoring decoy structure for this trajectory
# -recover and output the decoy structure to a PDB file
mc.recover_low(pose)
jd.output_decoy(pose)
# -export the final structure to PyMOL for each trajectory
pose.pdb_info().name(job_output + '_' + str(counter) + '_final')
pymover.apply(pose)
pymover.send_energy(pose) # see the total score in color
# -store the final score for this trajectory
scores[counter] = scorefxn(pose)
# 15. output the score evaluations
print('Original Score\t:\t', scores[0])
for i in range(1, len(scores)): # print out the job scores
print(job_output + '_' + str(i) + '\t:\t', scores[i])
return scores # for other protocols
def sample_single_loop_modeling(pdb_filename,
loop_begin,
loop_end,
loop_cutpoint,
frag_filename,
frag_length,
outer_cycles_low=2,
inner_cycles_low=5,
init_temp_low=2.0,
final_temp_low=0.8,
outer_cycles_high=5,
inner_cycles_high=10,
init_temp_high=2.2,
final_temp_high=0.6,
jobs=1,
job_output='loop_output'):
"""
Performs simple single loop construction on the input <pdb_filename>
with loop from <loop_begin> to <loop_end> with a
cutpoint at <loop_cutpoint> using fragments of length <frag_length>
in the file <frag_filename>. <jobs> trajectories are performed,
each using a low resolution (centroid) simulated annealing with
<outer_cycles> rounds and <inner_cycles> steps per round decrementing
"temperature" from <init_temp> to <final_temp> geometrically.
Output structures are named <job_output>_(job#).pdb.
"""
# 1. create a pose from the desired PDB file
p = Pose()
pose_from_file(p, pdb_filename)
# 2. create a reference copy of the pose in fullatom
starting_p = Pose()
starting_p.assign(p)
#### if you are constructing multiple loops simultaneously, changes will
#### occur in most of the steps below
# 3. create the Loop object
# (note: Loop objects merely specify residues, they contain no
# conformation data)
my_loop = protocols.loops.Loop(loop_begin, loop_end, loop_cutpoint)
#### if using multiple loops, add additional Loop objects
# 4. use the Loop to set the pose FoldTree
protocols.loops.set_single_loop_fold_tree(p, my_loop)
#### alternate FoldTree setup, if you uncomment the lines below,
#### comment-out the set_single_loop_foldtree line above (line 189)
#### -create an empty FoldTree
#ft = FoldTree()
#### -make it a single edge the length of pose
#ft.simple_tree(p.total_residue())
#### -insert a jump corresponding to the single loop region
#ft.add_jump(loop_begin - 2, loop_end + 2, loop_cutpoint)
#### -give the pose this FoldTree (set it to this object), this will
#### erase any previous FoldTree held by the pose
#p.fold_tree(ft)
#### there is also a fold_tree_from_loops method in exposed which sets up
#### a FoldTree but it is different from set_single_loop_foldtree in
#### that is creates jumps +/- 1 residue from their corresponding loop
#### endpoints and requires a third argument, the FoldTree to setup
# 5. sets the cut-point residues as cut-point variants
protocols.loops.add_single_cutpoint_variant(p, my_loop)
# 6. create the MoveMap, allow the loop region backbone and
# all chi torsions to be free
movemap = MoveMap()
movemap.set_bb_true_range(loop_begin, loop_end)
movemap.set_chi(True) # sets all chi torsions free
# 7. setup the fragment Mover
# this "try--except" is used to catch improper fragment files
try:
fragset = core.fragment.ConstantLengthFragSet(frag_length,
frag_filename)
#### the ConstantLengthFragSet is overloaded, this same
#### ConstantLengthFragSet can be obtained with different syntax
# to obtain custom fragments, see Generating Fragment Files below
except:
raise IOError('Make sure frag_length matches the fragments in\n\
frag_file and that frag_file is valid')
fragment_mover = protocols.simple_moves.ClassicFragmentMover(
fragset, movemap)
# 8. create a Mover for loop modeling using CCD (low resolution)
ccd_closure = protocols.loops.loop_closure.ccd.CCDLoopClosureMover(
my_loop, movemap)
# 9. create ScoreFunctions
# for centroid, use the default centroid ScoreFunction with chainbreak on
scorefxn_low = create_score_function('cen_std')
# the chainbreak ScoreType exists to penalize broken bonds
# try creating a broken pose in the interpreter and use a ScoreFunction
# with a chainbreak score to investigate its impact, the score is 0.0
# except when a bond is broken
# this penalizes failures caused by CCD failing to close the loop
scorefxn_low.set_weight(core.scoring.chainbreak, 1)
# for fullatom, used for packing and scoring final output
scorefxn_high = get_fa_scorefxn(
) # create_score_function_ws_patch('standard', 'score12')
# 10. setup sidechain packing Mover
task_pack = core.pack.task.TaskFactory.create_packer_task(starting_p)
task_pack.restrict_to_repacking() # prevents design, packing only
task_pack.or_include_current(True) # considers original sidechains
pack = protocols.minimization_packing.PackRotamersMover(
scorefxn_high, task_pack)
# 11. setup the high resolution refinement
# by creating a Loops object,
# (note: Loops is basically a list of Loop objects),
sample_loops = protocols.loops.Loops()
# giving it the loop to remodel,
sample_loops.add_loop(my_loop)
# and creating a fullatom CCD Mover (high resolution)
# this Mover is somewhat abnormal since it handles everything itself, it:
# -creates its own MoveMap for the loop regions
# -creates its own ScoreFunction (default to get_fa_scorefxn())
# -creates its own FoldTree for the pose based on the loops
# -creates its own MonteCarlo object for monitoring the pose
# -performs "simulated annealing" with 3 outer cycles and 90 inner
# cycles, very similar to the protocol outlined ere
# -creates its own backbone Movers (SmallMover, ShearMover)
# -creates its own PackRotamersMover, it does NOT restrict repacking
# to the loop regions and can alter all sidechain conformations
loop_refine = LoopMover_Refine_CCD(sample_loops)
# some of these parameters or objects can be set but the protocol
# executed by this Mover is effectively untouchable
#loop_refine.set_score_function(scorefxn_high) # in beta v2 and above
loop_refine.temp_initial(init_temp_high)
loop_refine.temp_final(init_temp_high)
loop_refine.outer_cycles(outer_cycles_high)
loop_refine.max_inner_cycles(inner_cycles_high)
# 12. create centroid <--> fullatom conversion Movers
to_centroid = SwitchResidueTypeSetMover('centroid')
to_fullatom = SwitchResidueTypeSetMover('fa_standard')
# and a Mover to recover sidechain conformations
# when a protocol samples backbone torsion space in centroid,
# the sidechain conformations are neglected, when it is transferred
# to fullatom, we typically set the sidechain conformations to their
# "original" values and perform sidechain packing,
# a ReturnSidechainMover saves a pose's sidechains (in this case
# staring_pose) and when applied, inserts these conformations
# into the input pose
recover_sidechains = protocols.simple_moves.ReturnSidechainMover(
starting_p)
# 13. create a reference copy of the pose in centroid
# the first stage of each trajectory is in centroid
# so a centroid reference is needed and the pose must start in centroid
to_centroid.apply(p)
starting_p_centroid = Pose()
starting_p_centroid.assign(p)
# 14. create the geometric "temperature" increment for simulated annealing
gamma = pow((final_temp_low / init_temp_low),
(1.0 / (outer_cycles_low * inner_cycles_low)))
# 15. create a PyMOLMover for exporting structures to PyMOL
pymov = PyMOLMover()
# uncomment the line below to load structures into successive states
#pymov.keep_history(True)
scorefxn_high(starting_p) # for exporting the scores
pymov.apply(starting_p)
pymov.send_energy(starting_p)
# 16. create a (Py)JobDistributor
# a PyJobDistributor uses the job_output argument to name all output files
# and performs the specified number (int) of jobs
# a ScoreFunction is required since the PyJobDistributor output .fasc file
# contains scoring information about each output PDB
jd = PyJobDistributor(job_output, jobs, scorefxn_high)
jd.native_pose = starting_p
# 17. perform the loop modeling protocol
counter = 0 # for exporting to PyMOL
while not jd.job_complete:
# a. set necessary variables for the new trajectory
# -reload the starting pose (centroid)
p.assign(starting_p_centroid)
# -change the pose's PDBInfo.name, for exporting to PyMOL
counter += 1
p.pdb_info().name(job_output + '_' + str(counter) + '_cen')
# -reset the starting "temperature" (to init_temp)
kT = init_temp_low
# -create a MonteCarlo object for this trajectory
# a MonteCarlo object assesses pass/fail by the Metropolis Criteria
# and also records information on the lowest scoring pose
mc = MonteCarlo(p, scorefxn_low, kT)
# b. "randomize" the loop
#### this section may change if you intend to use multiple loops or
#### alter the sampling method to "randomize" the loop
# -by breaking it open,
for i in range(loop_begin, loop_end + 1):
p.set_phi(i, -180)
p.set_psi(i, 180)
pymov.apply(p)
# -and then inserting fragments
# the number of insertions performed is somewhat arbitrary
for i in range(loop_begin, loop_end + 1):
fragment_mover.apply(p)
pymov.apply(p)
####
# low resolution loop modeling:
# c. simulated annealing incrementing kT geometrically
# from init_temp to final_temp
#### this section may change if you intend to use multiple loops or
#### alter the sampling method for low resolution modeling
for i in range(1, outer_cycles_low + 1):
# -start with the lowest scoring pose
mc.recover_low(p) # loads mc's lowest scoring pose into p
# -take several steps of in the simulated annealing by
for j in range(1, inner_cycles_low + 1):
# >increasing the "temperature"
kT = kT * gamma
mc.set_temperature(kT)
# >inserting a fragment,
fragment_mover.apply(p)
pymov.apply(p)
# >performing CCD,
ccd_closure.apply(p)
pymov.apply(p)
# >and assessing the Metropolis Criteria
mc.boltzmann(p)
####
# the LoopMover_Refine_CCD makes A LOT of moves, DO NOT expect to
# see useful results if you use the PyMOLMover keep_history option, the large
# number of intermediates will slow processing to a halt
# d. convert the best structure (lowest scoring) into fullatom by:
# -recovering the best (centroid) structure (lowest scoring),
mc.recover_low(p) # loads mc's lowest scoring pose into p
# -switching the ResidueTypeSet to fullatom (from centroid),
to_fullatom.apply(p)
# -recovering the original sidechain conformations,
recover_sidechains.apply(p)
# -and packing the result (since the backbone conformation has changed)
pack.apply(p)
pymov.apply(p)
p.pdb_info().name(job_output + '_' + str(counter) + '_fa')
# high-resolution refinement:
#### this section may change if you intend to use multiple loops or
#### alter the sampling method for high resolution refinement
# e. apply the LoopMover_Refine_CCD
loop_refine.apply(p)
# f. output the decoy (pose result from this trajectory)
# include the loop RMSD (Lrsmd)
# -output a PDB file using the PyJobDistributor
lrms = protocols.loops.loop_rmsd(p, starting_p, sample_loops, True)
jd.additional_decoy_info = ' Lrmsd: ' + str(lrms)
jd.output_decoy(p)
# -export the structure to PyMOL
pymov.apply(p)
pymov.send_energy(p)
# Low-Resolution (Centroid) Scoring
ras = pose_from_file("../test/data/workshops/6Q21.clean.pdb")
score2 = get_score_function()
print(score2(ras))
print(ras.residue(5))
switch = SwitchResidueTypeSetMover("centroid")
switch.apply(ras)
print(ras.residue(5))
score3 = create_score_function("score3")
print(score3(ras))
switch2 = SwitchResidueTypeSetMover("fa_standard")
switch2.apply(ras)
print(ras.residue(5))
# Protein Fragments
fragset = core.fragment.ConstantLengthFragSet(3)
fragset.read_fragment_file("../test/data/workshops/aat000_03_05.200_v1_3")
movemap = MoveMap()
movemap.set_bb(True)
mover_3mer = protocols.simple_moves.ClassicFragmentMover(fragset, movemap)
pose = pose_from_sequence("RFPMMSTFKVLLCGAVLSRIDAG", "centroid")
for res in range(1, p.total_residue() + 1):
pose.set_omega(res, 180)
mover_3mer.apply(pose)
print( "_____ Check point 1" )
pert_mover = rigid_moves.RigidBodyPerturbMover(jump_num, 8, 3)
#pert_mover.apply(pose)
randomize1 = rigid_moves.RigidBodyRandomizeMover(pose, jump_num, rigid_moves.partner_upstream)
randomize2 = rigid_moves.RigidBodyRandomizeMover(pose, jump_num, rigid_moves.partner_downstream)
print( "_____ Check point 2" )
#randomize1.apply(pose)
#randomize2.apply(pose)
slid = protocols.docking.DockingSlideIntoContact(jump_num)
slide = protocols.docking.FaDockingSlideIntoContact(jump_num)
slide.apply(pose)
movemap = MoveMap()
movemap.set_jump(jump_num, True)
scorefxn = create_score_function("ref2015")
scorefxn( pose )
print( "_____ Check point 3" )
print( 'Making MinMover...' )
min_mover = protocols.minimization_packing.MinMover()
min_mover.movemap(movemap)
min_mover.score_function(scorefxn)
#min_mover.apply(pose)
print( 'Done Applying MinMover!' )
